Open AccessSensitivity of the General Linear Model to Assumptions
Author(s) -
Muhammad Ahsan Asim Abdul Jabbar,
H. Ghorbaniparvar,
F. Ghorbaniparvar
Publication year - 2020
Publication title -
international journal of innovative science and modern engineering
Language(s) - English
Resource type - Journals
ISSN - 2319-6386
DOI - 10.35940/ijisme.e1210.036520
Subject(s) - gaussian noise , additive white gaussian noise , white noise , sensitivity (control systems) , gaussian , noise (video) , estimator , linear model , mathematics , computer science , statistics , algorithm , artificial intelligence , physics , engineering , electronic engineering , quantum mechanics , image (mathematics)
Non-Gaussian noise often causes in significant performance abatement for systems which are designed using Gaussian assumption. This report challenges the question of General Linear Model with White Gaussian Noise assumption in order to define the sensitivity of the performance of an optimal estimator. Gaussian noise models provide an important role in many signal processing applications. The Laplacian and Uniform signal are two worthy examples of noise that can be compared to the White Gaussian Noise, though the sensitivity which can be compared with any non-Gaussian. White Gaussian Noise has been considered for General Linear Models and deviation from whiteness would affect on our estimates under different circumstances. Moreover, new assumptions have been considered to generate different type of signals in order to evaluate the sensitivity of the General Linear Model.